首页> 外文期刊>Medical engineering & physics. >A novel method for discrimination between innocent and pathological heart murmurs
【24h】

A novel method for discrimination between innocent and pathological heart murmurs

机译:一种区分自然和病理性心脏杂音的新方法

获取原文
获取原文并翻译 | 示例
           

摘要

This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
机译:本文提出了一种新的方法,使用增长时间支持向量机(GTSVM)来区分无辜和病理性杂音。拟议的方法专门针对无辜杂音(IM)的特征而设计,因为它通常在收缩期早期就经常听到IM,因此将重点放在信号的早期部分。轻度至重度主动脉瓣狭窄(AS)和IM的个体为两组,以无杂音(NM)的正常个体为对照组。选择AS是因为其杂音与IM相似,特别是在轻度情况下。为了研究增长时间窗口的影响,使用重复随机子采样方法将GTSVM的性能与常规支持向量机(SVM)的性能进行了比较。发现对于GTSVM,分类率/灵敏度的平均值为88%/ 86%,对于SVM,为84%/ 83%。统计评估表明,与SVM相比,GTSVM显着提高了分类性能。 (C)2015年IPEM。由Elsevier Ltd.出版。保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号